{"id":"W3008003167","doi":"10.1016/j.chemosphere.2020.126304","title":"Development of a solid-phase microextraction method for fast analysis of cyclic volatile methylsiloxanes in water","year":2020,"lang":"en","type":"article","venue":"Chemosphere","topic":"Odor and Emission Control Technologies","field":"Chemical Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Jinan University; National Natural Science Foundation of China","keywords":"Solid-phase microextraction; Polydimethylsiloxane; Extraction (chemistry); Fiber; Chromatography; Octamethylcyclotetrasiloxane; Divinylbenzene; Wastewater; Contamination; Sample preparation; Materials science; Solid phase extraction; Gas chromatography–mass spectrometry; Chemistry; Environmental science; Polymer; Composite material; Environmental engineering; Mass spectrometry; Styrene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001288946,0.0001179572,0.000474048,0.0000448249,0.00001555437,0.000003206126,0.0001438801,0.0001259552,0.0002253429],"category_scores_gemma":[0.0001603857,0.00009126904,0.0001506655,0.0003870982,0.00001509472,0.00004718555,0.00004674763,0.0001016726,0.000002073669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002947404,"about_ca_system_score_gemma":0.00001622256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001316093,"about_ca_topic_score_gemma":0.00001179106,"domain_scores_codex":[0.9990843,0.000007305382,0.0004531479,0.0001970943,0.00009016525,0.0001680224],"domain_scores_gemma":[0.9995862,0.00008876446,0.0000891878,0.000138168,0.00005360373,0.00004408941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009932421,0.00006867946,0.000104593,0.0001544046,0.0002744799,5.195065e-7,0.001067408,0.002089463,0.9478742,0.00000701947,0.00006963775,0.04819029],"study_design_scores_gemma":[0.0007138352,0.00001710202,0.00004960426,0.0000147827,0.0001511536,1.338886e-7,0.0005089617,0.1399366,0.8560464,0.00002523581,0.002446389,0.00008986406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6888081,0.00009946488,0.310501,0.0002582752,0.00001749538,0.0001279631,0.00001629867,0.00007104292,0.0001003254],"genre_scores_gemma":[0.8602322,0.000002734749,0.1395889,0.00002085987,0.0000112016,0.00002691815,0.00002874758,0.00001220695,0.00007630503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.171424,"threshold_uncertainty_score":0.3721844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02894147408745426,"score_gpt":0.3419184642996811,"score_spread":0.3129769902122269,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}